Most people use SIFT features, although I've had better experiences with not scale-invariant ones. Basically you use an edge detector to find interesting points and then center your image patches around those points. That way you can also detect sub-images.
What you implemented is a hash method. There's tons to try from, but yours should work fine :)
The crucial step to making it fast is to hash your hashes. You convert your values into unary representation and then take a random subset of the bits as the new hash. Do that with 20-50 random samples and you get 20-50 hash tables. If any feature matches 2 or more out of those 50 hash tables, the feature will be very similar to one you already stored. This allows you to convert the abs(x-y)
Hope it helps, if you'd like to try out my self-developed image similarity search, drop me a mail at hajo at spratpix
发布评论
评论（1）
您想要使用的是：
大多数人都使用 SIFT 特征，尽管我在非尺度不变特征方面有更好的体验。基本上，您使用边缘检测器来查找有趣的点，然后将图像块集中在这些点周围。这样您还可以检测子图像。
你实现的是一个哈希方法。有很多东西可以尝试，但你的应该可以正常工作:)
使其快速的关键步骤是散列你的哈希值。您将值转换为一元表示形式，然后将位的随机子集作为新的哈希值。使用 20-50 个随机样本执行此操作，您将获得 20-50 个哈希表。如果任何特征与这 50 个哈希表中的 2 个或更多匹配，则该特征将与您已存储的特征非常相似。这允许您转换abs(xy)
希望它有帮助，如果你想尝试我自己开发的图像相似性搜索，请给我发邮件到hajo sprapix
What you want to use is:
Most people use SIFT features, although I've had better experiences with not scale-invariant ones. Basically you use an edge detector to find interesting points and then center your image patches around those points. That way you can also detect sub-images.
What you implemented is a hash method. There's tons to try from, but yours should work fine :)
The crucial step to making it fast is to hash your hashes. You convert your values into unary representation and then take a random subset of the bits as the new hash. Do that with 20-50 random samples and you get 20-50 hash tables. If any feature matches 2 or more out of those 50 hash tables, the feature will be very similar to one you already stored. This allows you to convert the abs(x-y)
Hope it helps, if you'd like to try out my self-developed image similarity search, drop me a mail at hajo at spratpix